After struggling with data entry in his game development project, the author discovered that reconstructing game assets as code rather than using the Unity editor significantly improved his workflow. By leveraging LLMs to assist in generating C# code from structured data, he was able to streamline the process and avoid burnout, ultimately allowing him to focus on problem analysis and solution development.

A developer shares insights from creating a VS Code extension called terminal-editor, which integrates a shell-like interface within the editor. The article emphasizes the importance of structured planning and testing strategies when working with large language models (LLMs) to enhance coding efficiency and reduce errors. It highlights the need for an effective feedback loop and the limitations of LLMs in maintaining code quality and handling complex problems.

The author shares insights from a month of experimenting with AI tools for software development, highlighting the limitations of large language models (LLMs) in producing production-ready code and their dependency on well-structured codebases. They discuss the challenges of integrating LLMs into workflows, the instability of AI products, and their mixed results across programming languages, emphasizing that while LLMs can aid in standard tasks, they struggle with unique or complex requirements.